Boosting a Weak Learning Algorithm by Majority

Early Prediction of Sepsis From Clinical Data Using Ratio and Power-Based Features

Nesaragi, N., & Patidar, S. (2020) · Critical Care Medicine · doi:10.1097/ccm.0000000000004691 · Cited by 36

Model and variable selection using machine learning methods with applications to childhood stunting in Bangladesh

Khan, J. R., Tomal, J., & Raheem, E. (2021) · Informatics for Health and Social Care · doi:10.1080/17538157.2021.1904938 · Cited by 40

A Short Tour of the Predictive Modeling Process

Kühn, M., & Johnson, K. (2013) · Springer eBooks · doi:10.1007/978-1-4614-6849-3_2 · Cited by 36

On the Power of Small-Depth Computation

Viola, E. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601983015 · Cited by 35

Trading strategy design in financial investment through a turning points prediction scheme

Li, X., Deng, Z., & Luo, J. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.11.014 · Cited by 35

Neural network language models for conversational speech recognition

Schwenk, H., & Gauvain, J. (2004) · Interspeech (USB) · doi:10.21437/interspeech.2004-497 · Cited by 35

Stochastic Attribute Selection Committees

Zheng, Z., & Webb, G. I. (1998) · Lecture notes in computer science · doi:10.1007/bfb0095063 · Cited by 35

Margins, Shrinkage, and BoostingPreprint

Telgarsky, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1303.4172 · Cited by 35

Anomaly-based error and intrusion detection in tabular data: No DNN outperforms tree-based classifiers

Zoppi, T., Gazzini, S., & Ceccarelli, A. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.06.051 · Cited by 39

A novel decorrelated neural network ensemble algorithm for face recognition

Dai, K., Zhao, J., & Cao, F. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.09.002 · Cited by 34

BoostWofE: A New Sequential Weights of Evidence Model Reducing the Effect of Conditional Dependency

Cheng, Q. (2015) · Mathematical Geosciences · doi:10.1007/s11004-014-9578-2 · Cited by 34

Learning to Sample: An Active Learning Framework

Shao, J., Wang, Q., & Liu, F. (2019) · Proceedings · doi:10.1109/icdm.2019.00064 · Cited by 34

Feature‐shared adaptive‐boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images

Wang, J., Chen, X., Lu, H., Zhang, L., Pan, J., Bao, Y. Q., Su, J., & Qian, D. (2020) · Medical Physics · doi:10.1002/mp.14068 · Cited by 34

Exploring the predictive capability of advanced machine learning in identifying severe disease phenotype in Salmonella enterica

Karanth, S., Tanui, C. K., Meng, J., & Pradhan, A. K. (2021) · Food Research International · doi:10.1016/j.foodres.2021.110817 · Cited by 35

Earthquake—explosion discrimination using genetic algorithm-based boosting approach

Orlic, N., & Lončarić, S. (2009) · Computers & Geosciences · doi:10.1016/j.cageo.2009.05.006 · Cited by 34

An automated airplane detection system for large panchromatic image with high spatial resolution

An, Z., Shi, Z., Teng, X., Yu, X., & Tang, W. (2014) · Optik · doi:10.1016/j.ijleo.2013.12.003 · Cited by 34

An ensemble-based evolutionary framework for coping with distributed intrusion detection

Gianluigi Folino, A., Pizzuti, C., & Spezzano, G. (2010) · Genetic Programming and Evolvable Machines · doi:10.1007/s10710-010-9101-6 · Cited by 34

PAC learning with irrelevant attributes

Dhagat, A., & Hellerstein, L. (2002) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1994.365704 · Cited by 34

Deep learning classification of coastal wetland hyperspectral image combined spectra and texture features: A case study of Huanghe (Yellow) River Estuary wetland

Hu, Y., Zhang, J., Ma, Y., Li, X., Sun, Q., & An, J. (2019) · Acta Oceanologica Sinica · doi:10.1007/s13131-019-1445-z · Cited by 33

Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes

Njage, P. M. K., Leekitcharoenphon, P., Hansen, L. T., Hendriksen, R. S., Faes, C., Aerts, M., & Hald, T. (2020) · Microorganisms · doi:10.3390/microorganisms8111772 · Cited by 34

A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-L1-Norm Interpolated Classifiers

Liang, T., & Sur, P. (2020) · The Annals of Statistics · doi:10.1214/22-aos2170 · Cited by 64

A Hybrid Sampling Approach for Imbalanced Binary and Multi-Class Data Using Clustering Analysis

Palli, A. S., Jaafar, J., Hashmani, M. A., Gomes, H. M., & Gilal, A. R. (2022) · IEEE Access · doi:10.1109/access.2022.3218463 · Cited by 33

Remedies for Severe Class Imbalance

Kühn, M., & Johnson, K. (2013) · Springer eBooks · doi:10.1007/978-1-4614-6849-3_16 · Cited by 35

Robust Boosting via Convex Optimization: Theory and Applications

Rätsch, G. (2007) · Cited by 33

From Isolation to Cooperation: An Alternative View of a System of Experts

Schaal, S., & Atkeson, C. G. (1995) · Cited by 33

Machine learning based approach for multimedia surveillance during fire emergencies

Saeed, F., Paul, A., Hong, W., & Seo, H. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7548-x · Cited by 32

In silico prediction of drug-induced developmental toxicity by using machine learning approaches

Zhang, H., Mao, J. W., Qi, H.-Z., & Ding, L. (2019) · Molecular Diversity · doi:10.1007/s11030-019-09991-y · Cited by 32

COMBINING REGRESSION ESTIMATORS: GA-BASED SELECTIVE NEURAL NETWORK ENSEMBLE

Zhou, Z., Wu, J., Tang, W., & Chen, Z.-Q. (2001) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026801000287 · Cited by 32

Improvement of neural network classifier using floating centroids

Wang, L., Yang, B., Chen, Y., Abraham, A., Sun, H., Chen, Z., & Wang, H. (2011) · Knowledge and Information Systems · doi:10.1007/s10115-011-0410-8 · Cited by 32

Linking genome to epigenome

Yuan, G. (2012) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1165 · Cited by 32

Mapping Percent Tree Mortality Due to Mountain Pine Beetle Damage

Long, J. A., & Lawrence, R. L. (2016) · Forest Science · doi:10.5849/forsci.15-046 · Cited by 32

A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning

Moghadas-Dastjerdi, H., Sha-E-Tallat, H. R., Sannachi, L., Naini, A. S., & Czarnota, G. J. (2020) · Scientific Reports · doi:10.1038/s41598-020-67823-8 · Cited by 33

Classification of medical data with a robust multi-level combination scheme

Tsirogiannis, G. L., Frossyniotis, D., Stoitsis, J. S., Golemati, S., Stafylopatis, A., & Nikita, K. S. (2005) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2004.1381020 · Cited by 32

Software development effort estimation using boosting algorithms and automatic tuning of hyperparameters with Optuna

Hassanali, M., Soltanaghaei, M., Gandomani, T. J., & Boroujeni, F. Z. (2024) · Journal of Software Evolution and Process · doi:10.1002/smr.2665 · Cited by 31

A parallel framework for software defect detection and metric selection on cloud computing

Ali, M. M., Huda, S., Abawajy, J., Alyahya, S., Al-Dossari, H. Z., & Yearwood, J. L. (2017) · Cluster Computing · doi:10.1007/s10586-017-0892-6 · Cited by 31

Longitudinal tear detection of conveyor belt under uneven light based on Haar-AdaBoost and Cascade algorithm

Wang, G., Zhang, L., Sun, H., & Zhu, C. (2020) · Measurement · doi:10.1016/j.measurement.2020.108341 · Cited by 31

Forecasting emergency department admissions

Rocha, C. N., & Rodrigues, F. (2021) · Journal of Intelligent Information Systems · doi:10.1007/s10844-021-00638-9 · Cited by 31

Prediction of the distillation temperatures of crude oils using 1H NMR and support vector regression with estimated confidence intervals

Filgueiras, P. R., Terra, L. A., de Castro, E. V. R., Oliveira, L. M. S. L., Dias, J. C., & Poppi, R. J. (2015) · Talanta · doi:10.1016/j.talanta.2015.04.046 · Cited by 33

Scaling Up a Boosting-Based Learner via Adaptive Sampling

Domingo, C., & Watanabe, O. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45571-x_37 · Cited by 31

Intelligent Optimization Methods for High-Dimensional Data Classification for Support Vector Machines

Ding, S., & Chen, L. (2010) · Intelligent Information Management · doi:10.4236/iim.2010.26043 · Cited by 30

Machine learning techniques for web intrusion detection — A comparison

Pham, T. S., Hoang, T.-H., & Canh, V. V. (2016) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse.2016.7758069 · Cited by 30

Top 10 data mining techniques in business applications: a brief survey

Lin, W., Ke, S., & Tsai, C. (2017) · Kybernetes · doi:10.1108/k-10-2016-0302 · Cited by 30

An ensemble predictive modeling framework for breast cancer classification

Nagarajan, R., & Upreti, M. (2017) · Methods · doi:10.1016/j.ymeth.2017.07.011 · Cited by 30

Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning

Moghadas-Dastjerdi, H., Rahman, S.-E.-T. H., Sannachi, L., Wright, F. C., Gandhi, S., Trudeau, M., Naini, A. S., & Czarnota, G. J. (2021) · Translational Oncology · doi:10.1016/j.tranon.2021.101183 · Cited by 30

Semantic Labeling of Places with Mobile Robots

Mozos, Ó. M. (2010) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-11210-2 · Cited by 30

Cortical Surface Thickness as a Classifier: Boosting for Autism Classification

Singh, V., Mukherjee, L., & Chung, M. K. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85988-8_119 · Cited by 30

Orthogonal decision trees

Kargupta, K., Park, B.-H., & Dutta, H. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.127 · Cited by 30

On boosting kernel regression

Marzio, M. D., & Taylor, C. C. (2007) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2007.10.005 · Cited by 30

Finite sample performance guarantees of fusers for function estimators

Rao, N. S. V. (2000) · Information Fusion · doi:10.1016/s1566-2535(00)00004-x · Cited by 30

Prospectivity and Uncertainty Analysis of Tungsten Polymetallogenic Mineral Resources in the Nanling Metallogenic Belt, South China: A Comparative Study of AdaBoost, GBDT, and XgBoost Algorithms

Li, T., Xia, Q., Ouyang, Y., Zeng, R., Liu, Q., & Li, T. (2024) · Natural Resources Research · doi:10.1007/s11053-024-10321-9 · Cited by 32

RWEN: response-weighted elastic net for prediction of chemosensitivity of cancer cell lines

Basu, A. B., Mitra, R., Liu, H., Schreiber, S. L., & Clemons, P. A. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty199 · Cited by 32

Object Detection Methods for Improving UAV Autonomy and Remote Sensing Applications

Aposporis, P. (2020) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam49781.2020.9381377 · Cited by 29

Effects of Concentrated Ownership and Owner Management on Small Business Debt Financing

Wu, Z., Hedges, P. L., & Zhang, S. (2007) · Journal of Small Business Management · doi:10.1111/j.1540-627x.2007.00221.x · Cited by 29

Having no mathematical model may not secure PUFs

Ganji, F., Tajik, S., Fäßler, F., & Seifert, J. (2017) · Journal of Cryptographic Engineering · doi:10.1007/s13389-017-0159-4 · Cited by 29

Leveraging for Regression

Duffy, N., & Helmbold, D. P. (2000) · Cited by 29

BOOSTED DECISION TREES, A POWERFUL EVENT CLASSIFIER

Roe, B. P., Yang, H.-J., & Zhu, J. (2006) · Conference on Statistical Problems in Particle Physics, Astrophysics and Cosmology (PHYSTAT) · doi:10.1142/9781860948985_0029 · Cited by 28

A strong hybrid AdaBoost classification algorithm for speaker recognition

Karthikeyan, V., & Priyadharsini, S. S. (2021) · Sadhana · doi:10.1007/s12046-021-01649-6 · Cited by 28

Applied Machine Learning in Operations Management

Bastani, H., Zhang, D., & Zhang, H. (2021) · Springer series in supply chain management · doi:10.1007/978-3-030-75729-8_7 · Cited by 28

On PAC Learning Algorithms for Rich Boolean Function Classes

Servedio, R. A. (2006) · Lecture notes in computer science · doi:10.1007/11750321_42 · Cited by 28

Kernel density classification and boosting: an L2 analysis

Marzio, M. D., & Taylor, C. C. (2005) · Statistics and Computing · doi:10.1007/s11222-005-6203-8 · Cited by 28

A niching evolutionary algorithm with adaptive negative correlation learning for neural network ensemble

Sheng, W., Shan, P., Chen, S., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.03.055 · Cited by 27

A Hybrid Method to Predict Postoperative Survival of Lung Cancer Using Improved SMOTE and Adaptive SVM

Shen, J., Wu, J., Xu, M., Gan, D., An, B., & Liu, F. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/2213194 · Cited by 27

Using random forests to predict passengers’ thermal comfort in underground train carriages

Huang, K., Lu, S., Li, X., & Chen, W. (2022) · Indoor and Built Environment · doi:10.1177/1420326x221110046 · Cited by 27

General Greedy De-Bias Learning

Han, X., Wang, S., Su, C., Huang, Q., & Tian, Q. C. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3240337 · Cited by 29

Learning from imperfect data

Hartono, P., & Hashimoto, S. (2005) · Applied Soft Computing · doi:10.1016/j.asoc.2005.07.005 · Cited by 27

Remote-sensing imagery classification using multiple classification algorithm-based AdaBoost

Dou, P., Chen, Y., & Yue, H. (2017) · International Journal of Remote Sensing · doi:10.1080/01431161.2017.1390276 · Cited by 27

Intelligent Internet of Things gateway supporting heterogeneous energy data management and processing

Diyan, M., Silva, B. N., Han, J., Cao, Z., & Han, K. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3919 · Cited by 27

A GP-based ensemble classification framework for time-changing streams of intrusion detection data

Gianluigi Folino, A., Pisani, F. S., & Pontieri, L. (2020) · Soft Computing · doi:10.1007/s00500-020-05200-3 · Cited by 27

SVM-Boosting based on Markov resampling: Theory and algorithm

Jiang, H., Zou, B., Xu, C., Xu, J., & Tang, Y. Y. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.07.036 · Cited by 27

Rough Set Based Decision Support—Models Easy to Interpret

Widz, S., & Ślȩzak, D. (2012) · Advanced information and knowledge processing · doi:10.1007/978-1-4471-2760-4_6 · Cited by 27

Study of ensemble learning-based fusion prognostics

Jianzhong, S., Hongfu, Z., Haibin, Y., & PECHT, M. G. (2010) · Prognostics and System Health Management Conference · doi:10.1109/phm.2010.5414582 · Cited by 28

Machine learning approaches for predicting shielding effectiveness of carbon fiber-reinforced mortars

Husnain, A., Iqbal, M., Ashraf, M. M., Alsekait, D. M., Javed, M. F., Alabduljabbar, H., & Elminaam, D. S. A. (2024) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2024.e03189 · Cited by 25

Optimized machine learning models for predicting the tensile strength of high-performance concrete

Kumar, D. R., Kumar, P., Thangavel, P., Wipulanusat, W., Thongchom, C., & Samui, P. (2025) · Journal of Structural Integrity and Maintenance · doi:10.1080/24705314.2025.2471682 · Cited by 26

Multi-Component Fusion Network for Small Object Detection in Remote Sensing Images

Liu, J., Yang, S., Tian, L., Guo, W., Zhou, B., Jia, J., & Ling, H. (2019) · IEEE Access · doi:10.1109/access.2019.2939488 · Cited by 25

A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case

Pomarico, D., Fanizzi, A., Amoroso, N., Bellotti, R., Biafora, A., Bove, S., Didonna, V., Forgia, D. L., Pastena, M. I., Tamborra, P., et al. (2021) · Mathematics · doi:10.3390/math9040410 · Cited by 26

An ensemble method for fuzzy rule-based classification systems

Soua, B., Borgi, A., & Tagina, M. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-012-0532-7 · Cited by 26

Drifting Games

Schapire, R. E. (2001) · Machine Learning · doi:10.1023/a:1010800213066 · Cited by 26

SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELS

WAND, M. P. (2009) · Australian & New Zealand Journal of Statistics · doi:10.1111/j.1467-842x.2009.00538.x · Cited by 25

A novel confidence-based multiclass boosting algorithm for mobile physical activity monitoring

Reiss, A., Hendeby, G., & Stricker, D. (2014) · Personal and Ubiquitous Computing · doi:10.1007/s00779-014-0816-x · Cited by 25

iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

Zhao, D., Teng, Z., Li, Y., & Chen, D. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.773202 · Cited by 25

Boosting method for nonlinear transformation models with censored survival data

Lu, W., & Li, L. (2008) · Biostatistics · doi:10.1093/biostatistics/kxn005 · Cited by 25

Ensemble SVR for prediction of time series

Deng, Y., Jin, X., & Zhong, Y. (2005) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2005.1527553 · Cited by 25

A survey of deep network techniques all classifiers can adopt

Ghods, A., & Cook, D. J. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00722-8 · Cited by 27

Monte Carlo theory as an explanation of bagging and boosting

Esposito, R., & Saitta, L. (2003) · Cited by 25

Combining multiple classifiers for wrapper feature selection

Chrysostomou, K., Chen, S. Y., & Liu, X. (2008) · International Journal of Data Mining Modelling and Management · doi:10.1504/ijdmmm.2008.022539 · Cited by 25

Automated language‐independent authorship verification (for Indo‐European languages)

Adamović, S., Miškovic, V., Milosavljević, M., Šarac, M., & Veinović, M. Đ. (2019) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24163 · Cited by 24

Arbitrary-angle bounding box based location for object detection in remote sensing image

Sun, F., Li, H., Liu, Z., Li, X., & Wu, Z. (2021) · European Journal of Remote Sensing · doi:10.1080/22797254.2021.1880975 · Cited by 24

Deselection of base-learners for statistical boosting—with an application to distributional regression

Strömer, A., Staerk, C., Klein, N., Weinhold, L., Titze, S. I., & Mayr, A. J. (2021) · Statistical Methods in Medical Research · doi:10.1177/09622802211051088 · Cited by 24

Safety helmet wearing status detection based on improved boosted random ferns

Yue, S., Zhang, Q., Shao, D.-Q., Yu, F., & Jinhua, B. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12014-y · Cited by 24

Weighted Hard and Soft Voting Ensemble Machine Learning Classifiers: Application to Anaemia Diagnosis

Awe, O. O., Opateye, G. O., Johnson, C. A. G., Tayo, O. T., & Dias, R. (2023) · STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health · doi:10.1007/978-3-031-41352-0_18 · Cited by 25

An optimized AdaBoost algorithm with atherosclerosis diagnostic applications: adaptive weight-adjustable boosting

Wang, S., Liu, W., Yang, S., & Huang, H. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-05951-y · Cited by 24

Hardness Amplification and the Approximate Degree of Constant-Depth Circuits

Bun, M., & Thaler, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-662-47672-7_22 · Cited by 27

Predicting adequacy of vancomycin regimens: A learning-based classification approach to improving clinical decision making

Hu, P. J., Wei, C., Cheng, T.-H., & Chen, J. (2006) · Decision Support Systems · doi:10.1016/j.dss.2006.02.003 · Cited by 24

NB+: An improved Naïve Bayesian algorithm

Balamurugan, S., Rajaram, R., Pramala, S., Rajalakshmi, S., Jeyendran, C., & Prakash, J. D. S. (2010) · Knowledge-Based Systems · doi:10.1016/j.knosys.2010.09.007 · Cited by 24

Data-driven aggregative schemes for multisource estimation fusion: a road travel time application

Faouzi, N. E. (2004) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.541336 · Cited by 24

MANIA—A Pattern Classification Toolbox for Neuroimaging Data

Grotegerd, D., Redlich, R., Almeida, J., Riemenschneider, M., Kugel, H., Arolt, V., & Dannlowski, U. (2014) · Neuroinformatics · doi:10.1007/s12021-014-9223-8 · Cited by 24

HAND WRITTEN CHARACTER RECOGNITION USING BACK PROPAGATION NETWORK

Devireddy, S. K., & Rao, S. A. (2009) · Cited by 24

A Distributed Intrusion Detection Framework Based on Evolved Specialized Ensembles of Classifiers

Gianluigi Folino, A., Pisani, F. S., & Sabatino, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-31204-0_21 · Cited by 24

An Incremental Ensemble Evolved by using Genetic Programming to Efficiently Detect Drifts in Cyber Security Datasets

Gianluigi Folino, A., Pisani, F. S., & Sabatino, P. (2016) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/2908961.2931682 · Cited by 24

Exploring the online underground marketplaces through topic-based social network and clustering

Huang, S., & Chen, H. (2016) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi.2016.7745458 · Cited by 25

An improved boosting algorithm and its implications on learning complexity

Freund, Y. (1992) · Conference on Learning Theory · doi:10.1145/130385.130429 · Cited by 61

The Strength of Weak Learnability

Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600

Boosting Simple Learners

Alon, N., Gonen, A., Hazan, E., & Moran, S. (2021) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3406325.3451030 · Cited by 11

The learning of a class of n-dimensional Boolean functions with a two input perceptron using the boosting algorithm

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Perceptron, Winnow, and PAC Learning

Servedio, R. A. (2002) · SIAM Journal on Computing · doi:10.1137/s0097539798340928 · Cited by 8